解释性AI让皮肤癌诊断更准，医生看懂后判断更稳。
Dermatologist-like explainable AI enhances melanoma diagnosis accuracy: eye-tracking study
- 用眼动追踪分析医生如何看AI给出的医学解释
- 解释性AI使诊断准确率比普通AI高2.8个百分点
- 对复杂病例依赖解释能降低认知负担，适合临床使用
人工智能（AI）系统已显著提升皮肤科医生对黑色素瘤的诊断准确性，而可解释AI（XAI）系统进一步增强了临床医生对AI决策的信任与信心。然而，仍缺乏对医生如何与AI及XAI工具互动的客观评估。本研究中，76名皮肤科医生参与阅读者研究，基于16张皮肤镜图像（含黑色素瘤与良性痣）进行诊断，使用提供领域特定详细解释的XAI系统，并通过眼动追踪技术分析其交互行为。对比无解释功能的标准AI系统，结果显示，XAI系统使平衡诊断准确率提升了2.8个百分点。此外，与AI/XAI系统意见不一致及面对复杂病灶时，医生的眼动固定次数增加，表明认知负荷上升。这些发现对临床实践、视觉任务AI工具设计以及医疗领域XAI的发展具有重要意义。
原文摘要 · Abstract (English)
Artificial intelligence (AI) systems have substantially improved dermatologists' diagnostic accuracy for melanoma, with explainable AI (XAI) systems further enhancing clinicians' confidence and trust in AI-driven decisions. Despite these advancements, there remains a critical need for objective evaluation of how dermatologists engage with both AI and XAI tools. In this study, 76 dermatologists participated in a reader study, diagnosing 16 dermoscopic images of melanomas and nevi using an XAI system that provides detailed, domain-specific explanations. Eye-tracking technology was employed to assess their interactions. Diagnostic performance was compared with that of a standard AI system lacking explanatory features. Our findings reveal that XAI systems improved balanced diagnostic accuracy by 2.8 percentage points relative to standard AI. Moreover, diagnostic disagreements with AI/XAI systems and complex lesions were associated with elevated cognitive load, as evidenced by increased ocular fixations. These insights have significant implications for clinical practice, the design of AI tools for visual tasks, and the broader development of XAI in medical diagnostics.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。